Bandwidth Estimation of a Network Path ET-4285 Measuring & Simulating the internet Bandwidth Estimation of a Network Path Group 4: S. Ngabonziza Rugemintwaza Mike Noordermeer Carolyn Simmonds Yalda Farazmand A.O. Adejuwon A.R.D.S. Silva Igor Dedic April 16, 2008

Outline GOALS INTRODUCTION DEFINITIONS MEASUREMENT TEST-BED MEASUREMENTS AND RESULTS CONCLUSION 2

Goals To estimate the bandwidth along an end-to-end network path using different tools. To compare the bandwidth estimation tools based on accuracy, time taken for measurement and impact on the network (intrusiveness). 3

Why Measure Bandwidth? To measure the optimum end-to-end performance of a network link. For route selection in overlay networks. For route selection in overlay networks for Quality of Service (QoS) verification, and traffic engineering. 4

Bandwidth Estimation Techniques Variable Packet Size (VPS) Probing: Capacity of individual hops along a path. RTT from the source to each hop of the path as a function of the packet size. Packet Pair/Train Dispersion (PPTD): End-to-end capacity. Self-Loading Periodic Streams (SLoPS ): End-to-end available bandwidth. Trains of Packet Pairs (TOPP): End-to-end available bandwidth. Analogous to SLoPS. 5

Bandwidth Measurement Metrics Capacity: maximum possible bandwidth a link can deliver. It is determined by the link with the minimum narrow link capacity (narrow link), C=C1. Bulk Transfer Capacity (BTC): maximum achievable throughput of a bulk transfer TCP connection. Available Bandwidth (ABW): maximum unused bandwidth of a link during a tight certain period of time; determined by the tight link link, A. 6 Metric selected: Available Bandwidth Metric selected: Available Bandwidth

ABW Estimation Tools used Pathload: Iterative probing. Attempts to create short-lived congestion conditions. Spruce: Send packet pairs spaced back-to-back according to the capacity of the tight link Pathchirp: Launch a number of packet ‘chirps’ from sender to receiver IGI/PTR: Send out packet trains with an increasing gap. Yaz: Send groups of packets at increasingly higher rates along a target path and measures the changes in packet spacing at the receiver. 7

Cross traffic (100 Mb/s link) /30 End to End (100 Mb/s link) WAN Link (10 Mb/s link) BrazilAmsterdamEgyptAustralia E 1/3 FE 0/1 FE 0/0 FE 0/1 RIOSYDNEY / / / /30 Measurement Test-bed 8 TOOL TOOL D-ITG D-ITG

Scenarios Scenario 1: Empty link (0 Mb/s cross traffic) Scenario 2: 1/4 full link (2,5 Mb/s cross traffic) Scenario 3: 1/2 full link (5 Mb/s cross traffic) Scenario 4: 3/4 full link (7,5 Mb/s cross traffic) Scenario 5: 19/20 full link (9,5 Mb/s cross traffic) 9

Performance Evaluation Results were analyzed in terms of the following metrics: ABW measured: Results were averaged and compared against reference value (Iptraf). Intrusiveness: Indication of the BW used by the tool for doing its work. Response time: Efficiency Relative error of ABW measured: Accuracy 10

ABW Estimation 11

Intrusiveness 12

Time per measurement 13

Relative Error 14

Comparison (1/2) All the tools underestimate the real ABW. Pathchirp and Spruce gave similar behaviour in their intrusive nature. Spruce, Pathchirp and Yaz returned their results at in constant time irrespective of the cross traffic. Yaz with very stable result also returns results in the least time. 15

Comparison (2/2) Pathload and IGI-PTR showed irregular behaviour returning measurements. IGI-PTR had the highest intrusiveness. With low cross traffic, Spruce, PathChirp, Pathload and Yaz had low error. Spruce and Pathchirp had less errors than Pathload, even though 50% error can be considered as unreliable for some applications. 16

Conclusions (1/2) In the end the exact bandwidth measurement tool to use depends on specific network setup and applications. In terms of stability and general performance, it is observed that for our measurement test-bed, Spruce and Pathchirp show better estimating capabilities and also perform very good when it comes to intrusiveness. 17

Conclusions (2/2) Spruce, PathChirp, Pathload and Yaz had low error for low cross traffic, we can say they had good estimation capabilities in these scenarios. Yaz gives the fastest results, it would be a good choice, especially for low/medium cross traffic conditions. For higher cross traffic scenarios all the tools had low accuracy and had some troubles (in terms of Relative Error) in estimating ABW. 18

Further research suggestions Test the tools with different setups. Estimating ABW in high capacity links is an important issue to be taken into account for a deep research. Understudy behavioural characteristics of tools. 19

THANK YOU!! 20